class_docstr: Base class for acquisition functions in Bayesian Optimization
  
  :param model: GPyOpt class of model
  :param space: GPyOpt class of domain
  :param optimizer: optimizer of the acquisition. Should be a GPyOpt optimizer
code: def __init__(self, model, space, optimizer, cost_withGradients=None):
      self.model = model
      self.space = space
      self.optimizer = optimizer
      self.analytical_gradient_acq = (
          self.analytical_gradient_prediction
          and self.model.analytical_gradient_prediction
      )  # flag from the model to test if gradients are available
  
      if cost_withGradients is None:
          self.cost_withGradients = constant_cost_withGradients
      else:
          self.cost_withGradients = cost_withGradients
  
file_path: GPyOpt/GPyOpt/acquisitions/base.py
imports: from ..core.task.cost import constant_cost_withGradients
label_desc: Use image node_id 1 to create a new AcquisitionBase object from inherited base classes: object with example: obj = AcquisitionBase(model, space, optimizer, cost_withGradients)